Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the...

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Bibliographic Details
Published in:	Energies (Basel) Vol. 12; no. 7; p. 1331
Main Authors:	Jiang, Peng, Dong, Jun, Huang, Hui
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.04.2019
Subjects:	Accuracy Algorithms Alternative energy sources bacterial foraging algorithm Coal Electricity Electricity distribution empirical mode decomposition Energy consumption energy forecasting extreme learning machine algorithm Fault diagnosis grey theory Income distribution Neural networks Optimization Pollutants Regression analysis renewable energy terminal power consumption Time series New Zealand Beijing China Iran China
ISSN:	1996-1073, 1996-1073
Online Access:	Get full text
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